lcmsPlot

lcmsPlot is an R package designed for visualising Liquid Chromatography-Mass Spectrometry (LC-MS) data with high-quality plots suitable for publication. The package enables users to generate and customise chromatograms, mass traces, spectra, and more with fine-tuned aesthetics and annotation options.

Overview

The motivation for developing a dedicated R package for LC–MS data visualization comes from the limitations of existing solutions such as base xcms plotting functions or generic plotting libraries. While xcms and related tools are powerful for data processing and feature detection, their plotting capabilities are often designed primarily for diagnostic purposes, offering limited flexibility and aesthetics.

Researchers frequently need publication-quality chromatograms, spectra, and comparative visualisations that clearly highlight subtle differences across samples or conditions, which can be cumbersome to generate with the default methods.

The lcmsPlot package addresses this gap by providing:

• A consistent, intuitive interface for LC–MS visualizations.
• Streamlined generation of high-quality, customisable plots.
• Improved reproducibility and reduced need for bespoke scripts.
• Interoperability with the existing ecosystem (e.g., MsExperiment, MSnbase)
• Better performance at scale.

Example

Suppose you want to plot chromatograms for a specified m/z and retention time window across all samples, grouped by sample class, with an indicator line at a specific retention time.

With xcms base plotting, this requires several manual steps: extracting chromatograms, arranging plots by sample, handling group colors, and adding annotations:

# Extract chromatograms
chr <- chromatogram(raw_data, mz = c(300, 305), rt = c(2500, 2550))

# Colors for groups
group_cols <- c("KO" = "red", "WT" = "blue")

# Plot setup: one panel per sample
n_samples <- length(chr)
n_col <- ceiling(sqrt(n_samples))
n_row <- ceiling(n_samples / n_col)

par(mfrow = c(n_row, n_col), mar = c(4, 4, 2, 1))

for (i in seq_along(chr)) {
  chri <- chr[[i]]
  df <- as.data.frame(chri)
  group <- pData(raw_data)$sample_group[i]
  
  # Plot
  plot(df$rtime, df$intensity,
       type = "l", col = group_cols[group],
       xlab = "Retention Time (s)", ylab = "Intensity",
       main = paste0(names(chr)[i], " (", group, ")"))
  
  # Add vertical line
  abline(v = 2520, lty = 2)
}

With lcmsPlot, the same task becomes much simpler and more reproducible:

library(lcmsPlot)

lcmsPlot(raw_data, sample_id_column = "sample_name") +
  chromatogram(features = rbind(c(
    mzmin = 300,
    mzmax = 305,
    rtmin = 2500,
    rtmax = 2550))) +
  rt_line(intercept = 2520) +
  arrange(group_by = "sample_group") +
  facets(facets = "sample_id", ncol = 3)

Installation

To install this package:

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("lcmsPlot")

To install the development version:

if (!require("remotes", quietly = TRUE))
    install.packages("remotes")

remotes::install_github("computational-metabolomics/lcmsPlot")